#pragma once


/* PLEASE NOTE:
*
* This file is no longer maintained, and actually had been removed from the more recent versions of Darknet in the
* Hank.ai repo.  However, it seems some projects and language bindings still use it.  If you are starting a *NEW*
* project or you're looking for the C++ API which is maintained, please use DarkHelp instead of this source file and
* library.  You can find more information on the DarkHelp library here:
*
* - https://www.ccoderun.ca/darkhelp/api/API.html
* - https://github.com/stephanecharette/DarkHelp#what-is-the-darkhelp-c-api
*/


#ifndef __cplusplus
#error "This requires a C++ compiler."
#endif

#define C_SHARP_MAX_OBJECTS 1000

struct bbox_t {
	unsigned int x, y, w, h;       // (x,y) - top-left corner, (w, h) - width & height of bounded box
	float prob;                    // confidence - probability that the object was found correctly
	unsigned int obj_id;           // class of object - from range [0, classes-1]
	unsigned int track_id;         // tracking id for video (0 - untracked, 1 - inf - tracked object)
	unsigned int frames_counter;   // counter of frames on which the object was detected
	float x_3d, y_3d, z_3d;        // center of object (in Meters) if ZED 3D Camera is used
};

struct image_t {
	int h;                        // height
	int w;                        // width
	int c;                        // number of chanels (3 - for RGB)
	float *data;                  // pointer to the image data
};

struct bbox_t_container {
	bbox_t candidates[C_SHARP_MAX_OBJECTS];
};


#include <memory>
#include <vector>
#include <deque>
#include <algorithm>
#include <chrono>
#include <string>
#include <sstream>
#include <iostream>
#include <cmath>
#include <opencv2/opencv.hpp>

extern "C"
{
	int init(const char *configurationFilename, const char *weightsFilename, int gpu, int batch_size);
	int detect_image(const char *filename, bbox_t_container &container);
	int detect_mat(const uint8_t* data, const size_t data_length, bbox_t_container &container);
	int dispose();
	int get_device_count();
	int get_device_name(int gpu, char* deviceName);
	bool built_with_cuda();
	bool built_with_cudnn();
	bool built_with_opencv();
	void send_json_custom(char const* send_buf, int port, int timeout);
}

class Detector {
	std::shared_ptr<void> detector_gpu_ptr;
	std::deque<std::vector<bbox_t>> prev_bbox_vec_deque;
	std::string _cfg_filename, _weight_filename;
public:
	const int cur_gpu_id;
	float nms = .4;
	bool wait_stream;

	Detector(std::string cfg_filename, std::string weight_filename, int gpu_id = 0, int batch_size = 1);
	~Detector();

	std::vector<bbox_t> detect(std::string image_filename, float thresh = 0.2, bool use_mean = false);
	std::vector<bbox_t> detect(image_t img, float thresh = 0.2, bool use_mean = false);
	std::vector<std::vector<bbox_t>> detectBatch(image_t img, int batch_size, int width, int height, float thresh, bool make_nms = true);
	static image_t load_image(std::string image_filename);
	static void free_image(image_t m);
	int get_net_width() const;
	int get_net_height() const;
	int get_net_color_depth() const;

	std::vector<bbox_t> tracking_id(std::vector<bbox_t> cur_bbox_vec, bool const change_history = true,
												int const frames_story = 5, int const max_dist = 40);

	void *get_cuda_context();

	std::vector<bbox_t> detect_resized(image_t img, int init_w, int init_h, float thresh = 0.2, bool use_mean = false)
	{
		if (img.data == NULL)
			throw std::runtime_error("Image is empty");
		auto detection_boxes = detect(img, thresh, use_mean);
		float wk = (float)init_w / img.w, hk = (float)init_h / img.h;
		for (auto &i : detection_boxes) i.x *= wk, i.w *= wk, i.y *= hk, i.h *= hk;
		return detection_boxes;
	}

	std::vector<bbox_t> detect(cv::Mat mat, float thresh = 0.2, bool use_mean = false)
	{
		if(mat.data == NULL)
			throw std::runtime_error("Image is empty");
		auto image_ptr = mat_to_image_resize(mat);
		return detect_resized(*image_ptr, mat.cols, mat.rows, thresh, use_mean);
	}

	std::shared_ptr<image_t> mat_to_image_resize(cv::Mat mat) const
	{
		if (mat.data == NULL) return std::shared_ptr<image_t>(NULL);

		cv::Size network_size = cv::Size(get_net_width(), get_net_height());
		cv::Mat det_mat;
		if (mat.size() != network_size)
			cv::resize(mat, det_mat, network_size);
		else
			det_mat = mat;  // only reference is copied

		return mat_to_image(det_mat);
	}

	static std::shared_ptr<image_t> mat_to_image(cv::Mat img_src)
	{
		cv::Mat img;
		if (img_src.channels() == 4) cv::cvtColor(img_src, img, cv::COLOR_RGBA2BGR);
		else if (img_src.channels() == 3) cv::cvtColor(img_src, img, cv::COLOR_RGB2BGR);
		else if (img_src.channels() == 1) cv::cvtColor(img_src, img, cv::COLOR_GRAY2BGR);
		else std::cerr << " Warning: img_src.channels() is not 1, 3 or 4. It is = " << img_src.channels() << std::endl;
		std::shared_ptr<image_t> image_ptr(new image_t, [](image_t *img) { free_image(*img); delete img; });
		*image_ptr = mat_to_image_custom(img);
		return image_ptr;
	}

private:

	static image_t mat_to_image_custom(cv::Mat mat)
	{
		int w = mat.cols;
		int h = mat.rows;
		int c = mat.channels();
		image_t im = make_image_custom(w, h, c);
		unsigned char *data = (unsigned char *)mat.data;
		int step = mat.step;
		for (int y = 0; y < h; ++y) {
			for (int k = 0; k < c; ++k) {
				for (int x = 0; x < w; ++x) {
					im.data[k*w*h + y*w + x] = data[y*step + x*c + k] / 255.0f;
				}
			}
		}
		return im;
	}

	static image_t make_empty_image(int w, int h, int c)
	{
		image_t out;
		out.data = 0;
		out.h = h;
		out.w = w;
		out.c = c;
		return out;
	}

	static image_t make_image_custom(int w, int h, int c)
	{
		image_t out = make_empty_image(w, h, c);
		out.data = (float *)calloc(h*w*c, sizeof(float));
		return out;
	}


public:

	bool send_json_http(std::vector<bbox_t> cur_bbox_vec, std::vector<std::string> obj_names, int frame_id,
		std::string filename = std::string(), int timeout = 400000, int port = 8070)
	{
		std::string send_str;

		char *tmp_buf = (char *)calloc(1024, sizeof(char));
		if (!filename.empty()) {
			sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"filename\":\"%s\", \n \"objects\": [ \n", frame_id, filename.c_str());
		}
		else {
			sprintf(tmp_buf, "{\n \"frame_id\":%d, \n \"objects\": [ \n", frame_id);
		}
		send_str = tmp_buf;
		free(tmp_buf);

		for (auto & i : cur_bbox_vec) {
			char *buf = (char *)calloc(2048, sizeof(char));

			sprintf(buf, "  {\"class_id\":%d, \"name\":\"%s\", \"absolute_coordinates\":{\"center_x\":%d, \"center_y\":%d, \"width\":%d, \"height\":%d}, \"confidence\":%f",
				i.obj_id, obj_names[i.obj_id].c_str(), i.x, i.y, i.w, i.h, i.prob);

			//sprintf(buf, "  {\"class_id\":%d, \"name\":\"%s\", \"relative_coordinates\":{\"center_x\":%f, \"center_y\":%f, \"width\":%f, \"height\":%f}, \"confidence\":%f",
			//    i.obj_id, obj_names[i.obj_id], i.x, i.y, i.w, i.h, i.prob);

			send_str += buf;

			if (!std::isnan(i.z_3d)) {
				sprintf(buf, "\n    , \"coordinates_in_meters\":{\"x_3d\":%.2f, \"y_3d\":%.2f, \"z_3d\":%.2f}",
					i.x_3d, i.y_3d, i.z_3d);
				send_str += buf;
			}

			send_str += "}\n";

			free(buf);
		}

		//send_str +=  "\n ] \n}, \n";
		send_str += "\n ] \n}";

		send_json_custom(send_str.c_str(), port, timeout);
		return true;
	}
};
// --------------------------------------------------------------------------------


#if defined(TRACK_OPTFLOW) && defined(OPENCV) && defined(GPU)

#include <opencv2/cudaoptflow.hpp>
#include <opencv2/cudaimgproc.hpp>
#include <opencv2/cudaarithm.hpp>
#include <opencv2/core/cuda.hpp>

class Tracker_optflow {
public:
	const int gpu_count;
	const int gpu_id;
	const int flow_error;


	Tracker_optflow(int _gpu_id = 0, int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
		gpu_count(cv::cuda::getCudaEnabledDeviceCount()), gpu_id(std::min(_gpu_id, gpu_count-1)),
		flow_error((_flow_error > 0)? _flow_error:(win_size*4))
	{
		int const old_gpu_id = cv::cuda::getDevice();
		cv::cuda::setDevice(gpu_id);

		stream = cv::cuda::Stream();

		sync_PyrLKOpticalFlow_gpu = cv::cuda::SparsePyrLKOpticalFlow::create();
		sync_PyrLKOpticalFlow_gpu->setWinSize(cv::Size(win_size, win_size));    // 9, 15, 21, 31
		sync_PyrLKOpticalFlow_gpu->setMaxLevel(max_level);        // +- 3 pt
		sync_PyrLKOpticalFlow_gpu->setNumIters(iterations);    // 2000, def: 30

		cv::cuda::setDevice(old_gpu_id);
	}

	// just to avoid extra allocations
	cv::cuda::GpuMat src_mat_gpu;
	cv::cuda::GpuMat dst_mat_gpu, dst_grey_gpu;
	cv::cuda::GpuMat prev_pts_flow_gpu, cur_pts_flow_gpu;
	cv::cuda::GpuMat status_gpu, err_gpu;

	cv::cuda::GpuMat src_grey_gpu;    // used in both functions
	cv::Ptr<cv::cuda::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow_gpu;
	cv::cuda::Stream stream;

	std::vector<bbox_t> cur_bbox_vec;
	std::vector<bool> good_bbox_vec_flags;
	cv::Mat prev_pts_flow_cpu;

	void update_cur_bbox_vec(std::vector<bbox_t> _cur_bbox_vec)
	{
		cur_bbox_vec = _cur_bbox_vec;
		good_bbox_vec_flags = std::vector<bool>(cur_bbox_vec.size(), true);
		cv::Mat prev_pts, cur_pts_flow_cpu;

		for (auto &i : cur_bbox_vec) {
			float x_center = (i.x + i.w / 2.0F);
			float y_center = (i.y + i.h / 2.0F);
			prev_pts.push_back(cv::Point2f(x_center, y_center));
		}

		if (prev_pts.rows == 0)
			prev_pts_flow_cpu = cv::Mat();
		else
			cv::transpose(prev_pts, prev_pts_flow_cpu);

		if (prev_pts_flow_gpu.cols < prev_pts_flow_cpu.cols) {
			prev_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());
			cur_pts_flow_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), prev_pts_flow_cpu.type());

			status_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_8UC1);
			err_gpu = cv::cuda::GpuMat(prev_pts_flow_cpu.size(), CV_32FC1);
		}

		prev_pts_flow_gpu.upload(cv::Mat(prev_pts_flow_cpu), stream);
	}


	void update_tracking_flow(cv::Mat src_mat, std::vector<bbox_t> _cur_bbox_vec)
	{
		int const old_gpu_id = cv::cuda::getDevice();
		if (old_gpu_id != gpu_id)
			cv::cuda::setDevice(gpu_id);

		if (src_mat.channels() == 1 || src_mat.channels() == 3 || src_mat.channels() == 4) {
			if (src_mat_gpu.cols == 0) {
				src_mat_gpu = cv::cuda::GpuMat(src_mat.size(), src_mat.type());
				src_grey_gpu = cv::cuda::GpuMat(src_mat.size(), CV_8UC1);
			}

			if (src_mat.channels() == 1) {
				src_mat_gpu.upload(src_mat, stream);
				src_mat_gpu.copyTo(src_grey_gpu);
			}
			else if (src_mat.channels() == 3) {
				src_mat_gpu.upload(src_mat, stream);
				cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGR2GRAY, 1, stream);
			}
			else if (src_mat.channels() == 4) {
				src_mat_gpu.upload(src_mat, stream);
				cv::cuda::cvtColor(src_mat_gpu, src_grey_gpu, CV_BGRA2GRAY, 1, stream);
			}
			else {
				std::cerr << " Warning: src_mat.channels() is not: 1, 3 or 4. It is = " << src_mat.channels() << " \n";
				return;
			}

		}
		update_cur_bbox_vec(_cur_bbox_vec);

		if (old_gpu_id != gpu_id)
			cv::cuda::setDevice(old_gpu_id);
	}


	std::vector<bbox_t> tracking_flow(cv::Mat dst_mat, bool check_error = true)
	{
		if (sync_PyrLKOpticalFlow_gpu.empty()) {
			std::cout << "sync_PyrLKOpticalFlow_gpu isn't initialized \n";
			return cur_bbox_vec;
		}

		int const old_gpu_id = cv::cuda::getDevice();
		if(old_gpu_id != gpu_id)
			cv::cuda::setDevice(gpu_id);

		if (dst_mat_gpu.cols == 0) {
			dst_mat_gpu = cv::cuda::GpuMat(dst_mat.size(), dst_mat.type());
			dst_grey_gpu = cv::cuda::GpuMat(dst_mat.size(), CV_8UC1);
		}

		//dst_grey_gpu.upload(dst_mat, stream);    // use BGR
		dst_mat_gpu.upload(dst_mat, stream);
		cv::cuda::cvtColor(dst_mat_gpu, dst_grey_gpu, CV_BGR2GRAY, 1, stream);

		if (src_grey_gpu.rows != dst_grey_gpu.rows || src_grey_gpu.cols != dst_grey_gpu.cols) {
			stream.waitForCompletion();
			src_grey_gpu = dst_grey_gpu.clone();
			cv::cuda::setDevice(old_gpu_id);
			return cur_bbox_vec;
		}

		////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu);    // OpenCV 2.4.x
		sync_PyrLKOpticalFlow_gpu->calc(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, err_gpu, stream);    // OpenCV 3.x

		cv::Mat cur_pts_flow_cpu;
		cur_pts_flow_gpu.download(cur_pts_flow_cpu, stream);

		dst_grey_gpu.copyTo(src_grey_gpu, stream);

		cv::Mat err_cpu, status_cpu;
		err_gpu.download(err_cpu, stream);
		status_gpu.download(status_cpu, stream);

		stream.waitForCompletion();

		std::vector<bbox_t> result_bbox_vec;

		if (err_cpu.cols == cur_bbox_vec.size() && status_cpu.cols == cur_bbox_vec.size())
		{
			for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
			{
				cv::Point2f cur_key_pt = cur_pts_flow_cpu.at<cv::Point2f>(0, i);
				cv::Point2f prev_key_pt = prev_pts_flow_cpu.at<cv::Point2f>(0, i);

				float moved_x = cur_key_pt.x - prev_key_pt.x;
				float moved_y = cur_key_pt.y - prev_key_pt.y;

				if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i])
					if (err_cpu.at<float>(0, i) < flow_error && status_cpu.at<unsigned char>(0, i) != 0 &&
						((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0)
					{
						cur_bbox_vec[i].x += moved_x + 0.5;
						cur_bbox_vec[i].y += moved_y + 0.5;
						result_bbox_vec.push_back(cur_bbox_vec[i]);
					}
					else good_bbox_vec_flags[i] = false;
				else good_bbox_vec_flags[i] = false;

				//if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]);
			}
		}

		cur_pts_flow_gpu.swap(prev_pts_flow_gpu);
		cur_pts_flow_cpu.copyTo(prev_pts_flow_cpu);

		if (old_gpu_id != gpu_id)
			cv::cuda::setDevice(old_gpu_id);

		return result_bbox_vec;
	}

};

#elif defined(TRACK_OPTFLOW) && defined(OPENCV)

#include <opencv2/video/tracking.hpp>

class Tracker_optflow {
public:
	const int flow_error;


	Tracker_optflow(int win_size = 15, int max_level = 3, int iterations = 8000, int _flow_error = -1) :
		flow_error((_flow_error > 0)? _flow_error:(win_size*4))
	{
		sync_PyrLKOpticalFlow = cv::SparsePyrLKOpticalFlow::create();
		sync_PyrLKOpticalFlow->setWinSize(cv::Size(win_size, win_size));    // 9, 15, 21, 31
		sync_PyrLKOpticalFlow->setMaxLevel(max_level);        // +- 3 pt

	}

	// just to avoid extra allocations
	cv::Mat dst_grey;
	cv::Mat prev_pts_flow, cur_pts_flow;
	cv::Mat status, err;

	cv::Mat src_grey;    // used in both functions
	cv::Ptr<cv::SparsePyrLKOpticalFlow> sync_PyrLKOpticalFlow;

	std::vector<bbox_t> cur_bbox_vec;
	std::vector<bool> good_bbox_vec_flags;

	void update_cur_bbox_vec(std::vector<bbox_t> _cur_bbox_vec)
	{
		cur_bbox_vec = _cur_bbox_vec;
		good_bbox_vec_flags = std::vector<bool>(cur_bbox_vec.size(), true);
		cv::Mat prev_pts, cur_pts_flow;

		for (auto &i : cur_bbox_vec) {
			float x_center = (i.x + i.w / 2.0F);
			float y_center = (i.y + i.h / 2.0F);
			prev_pts.push_back(cv::Point2f(x_center, y_center));
		}

		if (prev_pts.rows == 0)
			prev_pts_flow = cv::Mat();
		else
			cv::transpose(prev_pts, prev_pts_flow);
	}


	void update_tracking_flow(cv::Mat new_src_mat, std::vector<bbox_t> _cur_bbox_vec)
	{
		if (new_src_mat.channels() == 1) {
			src_grey = new_src_mat.clone();
		}
		else if (new_src_mat.channels() == 3) {
			cv::cvtColor(new_src_mat, src_grey, CV_BGR2GRAY, 1);
		}
		else if (new_src_mat.channels() == 4) {
			cv::cvtColor(new_src_mat, src_grey, CV_BGRA2GRAY, 1);
		}
		else {
			std::cerr << " Warning: new_src_mat.channels() is not: 1, 3 or 4. It is = " << new_src_mat.channels() << " \n";
			return;
		}
		update_cur_bbox_vec(_cur_bbox_vec);
	}


	std::vector<bbox_t> tracking_flow(cv::Mat new_dst_mat, bool check_error = true)
	{
		if (sync_PyrLKOpticalFlow.empty()) {
			std::cout << "sync_PyrLKOpticalFlow isn't initialized \n";
			return cur_bbox_vec;
		}

		cv::cvtColor(new_dst_mat, dst_grey, CV_BGR2GRAY, 1);

		if (src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols) {
			src_grey = dst_grey.clone();
			//std::cerr << " Warning: src_grey.rows != dst_grey.rows || src_grey.cols != dst_grey.cols \n";
			return cur_bbox_vec;
		}

		if (prev_pts_flow.cols < 1) {
			return cur_bbox_vec;
		}

		////sync_PyrLKOpticalFlow_gpu.sparse(src_grey_gpu, dst_grey_gpu, prev_pts_flow_gpu, cur_pts_flow_gpu, status_gpu, &err_gpu);    // OpenCV 2.4.x
		sync_PyrLKOpticalFlow->calc(src_grey, dst_grey, prev_pts_flow, cur_pts_flow, status, err);    // OpenCV 3.x

		dst_grey.copyTo(src_grey);

		std::vector<bbox_t> result_bbox_vec;

		if (err.rows == cur_bbox_vec.size() && status.rows == cur_bbox_vec.size())
		{
			for (size_t i = 0; i < cur_bbox_vec.size(); ++i)
			{
				cv::Point2f cur_key_pt = cur_pts_flow.at<cv::Point2f>(0, i);
				cv::Point2f prev_key_pt = prev_pts_flow.at<cv::Point2f>(0, i);

				float moved_x = cur_key_pt.x - prev_key_pt.x;
				float moved_y = cur_key_pt.y - prev_key_pt.y;

				if (abs(moved_x) < 100 && abs(moved_y) < 100 && good_bbox_vec_flags[i])
					if (err.at<float>(0, i) < flow_error && status.at<unsigned char>(0, i) != 0 &&
						((float)cur_bbox_vec[i].x + moved_x) > 0 && ((float)cur_bbox_vec[i].y + moved_y) > 0)
					{
						cur_bbox_vec[i].x += moved_x + 0.5;
						cur_bbox_vec[i].y += moved_y + 0.5;
						result_bbox_vec.push_back(cur_bbox_vec[i]);
					}
					else good_bbox_vec_flags[i] = false;
				else good_bbox_vec_flags[i] = false;

				//if(!check_error && !good_bbox_vec_flags[i]) result_bbox_vec.push_back(cur_bbox_vec[i]);
			}
		}

		prev_pts_flow = cur_pts_flow.clone();

		return result_bbox_vec;
	}

};
#else

class Tracker_optflow {};

#endif    // defined(TRACK_OPTFLOW) && defined(OPENCV)


static cv::Scalar obj_id_to_color(int obj_id) {
	int const colors[6][3] = { { 1,0,1 },{ 0,0,1 },{ 0,1,1 },{ 0,1,0 },{ 1,1,0 },{ 1,0,0 } };
	int const offset = obj_id * 123457 % 6;
	int const color_scale = 150 + (obj_id * 123457) % 100;
	cv::Scalar color(colors[offset][0], colors[offset][1], colors[offset][2]);
	color *= color_scale;
	return color;
}

class preview_boxes_t {
	enum { frames_history = 30 };    // how long to keep the history saved

	struct preview_box_track_t {
		unsigned int track_id, obj_id, last_showed_frames_ago;
		bool current_detection;
		bbox_t bbox;
		cv::Mat mat_obj, mat_resized_obj;
		preview_box_track_t() : track_id(0), obj_id(0), last_showed_frames_ago(frames_history), current_detection(false) {}
	};
	std::vector<preview_box_track_t> preview_box_track_id;
	size_t const preview_box_size, bottom_offset;
	bool const one_off_detections;
public:
	preview_boxes_t(size_t _preview_box_size = 100, size_t _bottom_offset = 100, bool _one_off_detections = false) :
		preview_box_size(_preview_box_size), bottom_offset(_bottom_offset), one_off_detections(_one_off_detections)
	{}

	void set(cv::Mat src_mat, std::vector<bbox_t> result_vec)
	{
		size_t const count_preview_boxes = src_mat.cols / preview_box_size;
		if (preview_box_track_id.size() != count_preview_boxes) preview_box_track_id.resize(count_preview_boxes);

		// increment frames history
		for (auto &i : preview_box_track_id)
			i.last_showed_frames_ago = std::min((unsigned)frames_history, i.last_showed_frames_ago + 1);

		// occupy empty boxes
		for (auto &k : result_vec) {
			bool found = false;
			// find the same (track_id)
			for (auto &i : preview_box_track_id) {
				if (i.track_id == k.track_id) {
					if (!one_off_detections) i.last_showed_frames_ago = 0; // for tracked objects
					found = true;
					break;
				}
			}
			if (!found) {
				// find empty box
				for (auto &i : preview_box_track_id) {
					if (i.last_showed_frames_ago == frames_history) {
						if (!one_off_detections && k.frames_counter == 0) break; // don't show if obj isn't tracked yet
						i.track_id = k.track_id;
						i.obj_id = k.obj_id;
						i.bbox = k;
						i.last_showed_frames_ago = 0;
						break;
					}
				}
			}
		}

		// draw preview box (from old or current frame)
		for (size_t i = 0; i < preview_box_track_id.size(); ++i)
		{
			// get object image
			cv::Mat dst = preview_box_track_id[i].mat_resized_obj;
			preview_box_track_id[i].current_detection = false;

			for (auto &k : result_vec) {
				if (preview_box_track_id[i].track_id == k.track_id) {
					if (one_off_detections && preview_box_track_id[i].last_showed_frames_ago > 0) {
						preview_box_track_id[i].last_showed_frames_ago = frames_history; break;
					}
					bbox_t b = k;
					cv::Rect r(b.x, b.y, b.w, b.h);
					cv::Rect img_rect(cv::Point2i(0, 0), src_mat.size());
					cv::Rect rect_roi = r & img_rect;
					if (rect_roi.width > 1 || rect_roi.height > 1) {
						cv::Mat roi = src_mat(rect_roi);
						cv::resize(roi, dst, cv::Size(preview_box_size, preview_box_size), cv::INTER_NEAREST);
						preview_box_track_id[i].mat_obj = roi.clone();
						preview_box_track_id[i].mat_resized_obj = dst.clone();
						preview_box_track_id[i].current_detection = true;
						preview_box_track_id[i].bbox = k;
					}
					break;
				}
			}
		}
	}


	void draw(cv::Mat draw_mat, bool show_small_boxes = false)
	{
		// draw preview box (from old or current frame)
		for (size_t i = 0; i < preview_box_track_id.size(); ++i)
		{
			auto &prev_box = preview_box_track_id[i];

			// draw object image
			cv::Mat dst = prev_box.mat_resized_obj;
			if (prev_box.last_showed_frames_ago < frames_history &&
				dst.size() == cv::Size(preview_box_size, preview_box_size))
			{
				cv::Rect dst_rect_roi(cv::Point2i(i * preview_box_size, draw_mat.rows - bottom_offset), dst.size());
				cv::Mat dst_roi = draw_mat(dst_rect_roi);
				dst.copyTo(dst_roi);

				cv::Scalar color = obj_id_to_color(prev_box.obj_id);
				int thickness = (prev_box.current_detection) ? 5 : 1;
				cv::rectangle(draw_mat, dst_rect_roi, color, thickness);

				unsigned int const track_id = prev_box.track_id;
				std::string track_id_str = (track_id > 0) ? std::to_string(track_id) : "";
				putText(draw_mat, track_id_str, dst_rect_roi.tl() - cv::Point2i(-4, 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.9, cv::Scalar(0, 0, 0), 2);

				std::string size_str = std::to_string(prev_box.bbox.w) + "x" + std::to_string(prev_box.bbox.h);
				putText(draw_mat, size_str, dst_rect_roi.tl() + cv::Point2i(0, 12), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);

				if (!one_off_detections && prev_box.current_detection) {
					cv::line(draw_mat, dst_rect_roi.tl() + cv::Point2i(preview_box_size, 0),
						cv::Point2i(prev_box.bbox.x, prev_box.bbox.y + prev_box.bbox.h),
						color);
				}

				if (one_off_detections && show_small_boxes) {
					cv::Rect src_rect_roi(cv::Point2i(prev_box.bbox.x, prev_box.bbox.y),
						cv::Size(prev_box.bbox.w, prev_box.bbox.h));
					unsigned int const color_history = (255 * prev_box.last_showed_frames_ago) / frames_history;
					color = cv::Scalar(255 - 3 * color_history, 255 - 2 * color_history, 255 - 1 * color_history);
					if (prev_box.mat_obj.size() == src_rect_roi.size()) {
						prev_box.mat_obj.copyTo(draw_mat(src_rect_roi));
					}
					cv::rectangle(draw_mat, src_rect_roi, color, thickness);
					putText(draw_mat, track_id_str, src_rect_roi.tl() - cv::Point2i(0, 10), cv::FONT_HERSHEY_COMPLEX_SMALL, 0.8, cv::Scalar(0, 0, 0), 1);
				}
			}
		}
	}
};


class track_kalman_t
{
	int track_id_counter;
	std::chrono::steady_clock::time_point global_last_time;
	float dT;

public:
	int max_objects;    // max objects for tracking
	int min_frames;     // min frames to consider an object as detected
	const float max_dist;   // max distance (in px) to track with the same ID
	cv::Size img_size;  // max value of x,y,w,h

	struct tst_t {
		int track_id;
		int state_id;
		std::chrono::steady_clock::time_point last_time;
		int detection_count;
		tst_t() : track_id(-1), state_id(-1) {}
	};
	std::vector<tst_t> track_id_state_id_time;
	std::vector<bbox_t> result_vec_pred;

	struct one_kalman_t;
	std::vector<one_kalman_t> kalman_vec;

	struct one_kalman_t
	{
		cv::KalmanFilter kf;
		cv::Mat state;
		cv::Mat meas;
		int measSize, stateSize, contrSize;

		void set_delta_time(float dT) {
			kf.transitionMatrix.at<float>(2) = dT;
			kf.transitionMatrix.at<float>(9) = dT;
		}

		void set(bbox_t box)
		{
			initialize_kalman();

			kf.errorCovPre.at<float>(0) = 1; // px
			kf.errorCovPre.at<float>(7) = 1; // px
			kf.errorCovPre.at<float>(14) = 1;
			kf.errorCovPre.at<float>(21) = 1;
			kf.errorCovPre.at<float>(28) = 1; // px
			kf.errorCovPre.at<float>(35) = 1; // px

			state.at<float>(0) = box.x;
			state.at<float>(1) = box.y;
			state.at<float>(2) = 0;
			state.at<float>(3) = 0;
			state.at<float>(4) = box.w;
			state.at<float>(5) = box.h;
			// <<<< Initialization

			kf.statePost = state;
		}

		// Kalman.correct() calculates: statePost = statePre + gain * (z(k)-measurementMatrix*statePre);
		// corrected state (x(k)): x(k)=x'(k)+K(k)*(z(k)-H*x'(k))
		void correct(bbox_t box) {
			meas.at<float>(0) = box.x;
			meas.at<float>(1) = box.y;
			meas.at<float>(2) = box.w;
			meas.at<float>(3) = box.h;

			kf.correct(meas);

			bbox_t new_box = predict();
			if (new_box.w == 0 || new_box.h == 0) {
				set(box);
				//std::cerr << " force set(): track_id = " << box.track_id <<
				//    ", x = " << box.x << ", y = " << box.y << ", w = " << box.w << ", h = " << box.h << std::endl;
			}
		}

		// Kalman.predict() calculates: statePre = TransitionMatrix * statePost;
		// predicted state (x'(k)): x(k)=A*x(k-1)+B*u(k)
		bbox_t predict() {
			bbox_t box;
			state = kf.predict();

			box.x = state.at<float>(0);
			box.y = state.at<float>(1);
			box.w = state.at<float>(4);
			box.h = state.at<float>(5);
			return box;
		}

		void initialize_kalman()
		{
			kf = cv::KalmanFilter(stateSize, measSize, contrSize, CV_32F);

			// Transition State Matrix A
			// Note: set dT at each processing step!
			// [ 1 0 dT 0  0 0 ]
			// [ 0 1 0  dT 0 0 ]
			// [ 0 0 1  0  0 0 ]
			// [ 0 0 0  1  0 0 ]
			// [ 0 0 0  0  1 0 ]
			// [ 0 0 0  0  0 1 ]
			cv::setIdentity(kf.transitionMatrix);

			// Measure Matrix H
			// [ 1 0 0 0 0 0 ]
			// [ 0 1 0 0 0 0 ]
			// [ 0 0 0 0 1 0 ]
			// [ 0 0 0 0 0 1 ]
			kf.measurementMatrix = cv::Mat::zeros(measSize, stateSize, CV_32F);
			kf.measurementMatrix.at<float>(0) = 1.0f;
			kf.measurementMatrix.at<float>(7) = 1.0f;
			kf.measurementMatrix.at<float>(16) = 1.0f;
			kf.measurementMatrix.at<float>(23) = 1.0f;

			// Process Noise Covariance Matrix Q - result smoother with lower values (1e-2)
			// [ Ex   0   0     0     0    0  ]
			// [ 0    Ey  0     0     0    0  ]
			// [ 0    0   Ev_x  0     0    0  ]
			// [ 0    0   0     Ev_y  0    0  ]
			// [ 0    0   0     0     Ew   0  ]
			// [ 0    0   0     0     0    Eh ]
			//cv::setIdentity(kf.processNoiseCov, cv::Scalar(1e-3));
			kf.processNoiseCov.at<float>(0) = 1e-2;
			kf.processNoiseCov.at<float>(7) = 1e-2;
			kf.processNoiseCov.at<float>(14) = 1e-2;// 5.0f;
			kf.processNoiseCov.at<float>(21) = 1e-2;// 5.0f;
			kf.processNoiseCov.at<float>(28) = 5e-3;
			kf.processNoiseCov.at<float>(35) = 5e-3;

			// Measures Noise Covariance Matrix R - result smoother with higher values (1e-1)
			cv::setIdentity(kf.measurementNoiseCov, cv::Scalar(1e-1));

			//cv::setIdentity(kf.errorCovPost, cv::Scalar::all(1e-2));
			// <<<< Kalman Filter

			set_delta_time(0);
		}


		one_kalman_t(int _stateSize = 6, int _measSize = 4, int _contrSize = 0) :
			kf(_stateSize, _measSize, _contrSize, CV_32F), measSize(_measSize), stateSize(_stateSize), contrSize(_contrSize)
		{
			state = cv::Mat(stateSize, 1, CV_32F);  // [x,y,v_x,v_y,w,h]
			meas = cv::Mat(measSize, 1, CV_32F);    // [z_x,z_y,z_w,z_h]
			//cv::Mat procNoise(stateSize, 1, type)
			// [E_x,E_y,E_v_x,E_v_y,E_w,E_h]

			initialize_kalman();
		}
	};
	// ------------------------------------------



	track_kalman_t(int _max_objects = 1000, int _min_frames = 3, float _max_dist = 40, cv::Size _img_size = cv::Size(10000, 10000)) :
		track_id_counter(0), max_objects(_max_objects), min_frames(_min_frames), max_dist(_max_dist), img_size(_img_size)
	{
		kalman_vec.resize(max_objects);
		track_id_state_id_time.resize(max_objects);
		result_vec_pred.resize(max_objects);
	}

	float calc_dt() {
		dT = std::chrono::duration<double>(std::chrono::steady_clock::now() - global_last_time).count();
		return dT;
	}

	static float get_distance(float src_x, float src_y, float dst_x, float dst_y) {
		return sqrtf((src_x - dst_x)*(src_x - dst_x) + (src_y - dst_y)*(src_y - dst_y));
	}

	void clear_old_states() {
		// clear old bboxes
		for (size_t state_id = 0; state_id < track_id_state_id_time.size(); ++state_id)
		{
			float time_sec = std::chrono::duration<double>(std::chrono::steady_clock::now() - track_id_state_id_time[state_id].last_time).count();
			float time_wait = 0.5;    // 0.5 second
			if (track_id_state_id_time[state_id].track_id > -1)
			{
				if ((result_vec_pred[state_id].x > img_size.width) ||
					(result_vec_pred[state_id].y > img_size.height))
				{
					track_id_state_id_time[state_id].track_id = -1;
				}

				if (time_sec >= time_wait || track_id_state_id_time[state_id].detection_count < 0) {
					//std::cerr << " remove track_id = " << track_id_state_id_time[state_id].track_id << ", state_id = " << state_id << std::endl;
					track_id_state_id_time[state_id].track_id = -1; // remove bbox
				}
			}
		}
	}

	tst_t get_state_id(bbox_t find_box, std::vector<bool> &busy_vec)
	{
		tst_t tst;
		tst.state_id = -1;

		float min_dist = std::numeric_limits<float>::max();

		for (size_t i = 0; i < max_objects; ++i)
		{
			if (track_id_state_id_time[i].track_id > -1 && result_vec_pred[i].obj_id == find_box.obj_id && busy_vec[i] == false)
			{
				bbox_t pred_box = result_vec_pred[i];

				float dist = get_distance(pred_box.x, pred_box.y, find_box.x, find_box.y);

				float movement_dist = std::max(max_dist, static_cast<float>(std::max(pred_box.w, pred_box.h)) );

				if ((dist < movement_dist) && (dist < min_dist)) {
					min_dist = dist;
					tst.state_id = i;
				}
			}
		}

		if (tst.state_id > -1) {
			track_id_state_id_time[tst.state_id].last_time = std::chrono::steady_clock::now();
			track_id_state_id_time[tst.state_id].detection_count = std::max(track_id_state_id_time[tst.state_id].detection_count + 2, 10);
			tst = track_id_state_id_time[tst.state_id];
			busy_vec[tst.state_id] = true;
		}
		else {
			//std::cerr << " Didn't find: obj_id = " << find_box.obj_id << ", x = " << find_box.x << ", y = " << find_box.y <<
			//    ", track_id_counter = " << track_id_counter << std::endl;
		}

		return tst;
	}

	tst_t new_state_id(std::vector<bool> &busy_vec)
	{
		tst_t tst;
		// find empty cell to add new track_id
		auto it = std::find_if(track_id_state_id_time.begin(), track_id_state_id_time.end(), [&](tst_t &v) { return v.track_id == -1; });
		if (it != track_id_state_id_time.end()) {
			it->state_id = it - track_id_state_id_time.begin();
			//it->track_id = track_id_counter++;
			it->track_id = 0;
			it->last_time = std::chrono::steady_clock::now();
			it->detection_count = 1;
			tst = *it;
			busy_vec[it->state_id] = true;
		}

		return tst;
	}

	std::vector<tst_t> find_state_ids(std::vector<bbox_t> result_vec)
	{
		std::vector<tst_t> tst_vec(result_vec.size());

		std::vector<bool> busy_vec(max_objects, false);

		for (size_t i = 0; i < result_vec.size(); ++i)
		{
			tst_t tst = get_state_id(result_vec[i], busy_vec);
			int state_id = tst.state_id;
			int track_id = tst.track_id;

			// if new state_id
			if (state_id < 0) {
				tst = new_state_id(busy_vec);
				state_id = tst.state_id;
				track_id = tst.track_id;
				if (state_id > -1) {
					kalman_vec[state_id].set(result_vec[i]);
					//std::cerr << " post: ";
				}
			}

			//std::cerr << " track_id = " << track_id << ", state_id = " << state_id <<
			//    ", x = " << result_vec[i].x << ", det_count = " << tst.detection_count << std::endl;

			if (state_id > -1) {
				tst_vec[i] = tst;
				result_vec_pred[state_id] = result_vec[i];
				result_vec_pred[state_id].track_id = track_id;
			}
		}

		return tst_vec;
	}

	std::vector<bbox_t> predict()
	{
		clear_old_states();
		std::vector<bbox_t> result_vec;

		for (size_t i = 0; i < max_objects; ++i)
		{
			tst_t tst = track_id_state_id_time[i];
			if (tst.track_id > -1) {
				bbox_t box = kalman_vec[i].predict();

				result_vec_pred[i].x = box.x;
				result_vec_pred[i].y = box.y;
				result_vec_pred[i].w = box.w;
				result_vec_pred[i].h = box.h;

				if (tst.detection_count >= min_frames)
				{
					if (track_id_state_id_time[i].track_id == 0) {
						track_id_state_id_time[i].track_id = ++track_id_counter;
						result_vec_pred[i].track_id = track_id_counter;
					}

					result_vec.push_back(result_vec_pred[i]);
				}
			}
		}
		//std::cerr << "         result_vec.size() = " << result_vec.size() << std::endl;

		//global_last_time = std::chrono::steady_clock::now();

		return result_vec;
	}


	std::vector<bbox_t> correct(std::vector<bbox_t> result_vec)
	{
		calc_dt();
		clear_old_states();

		for (size_t i = 0; i < max_objects; ++i)
			track_id_state_id_time[i].detection_count--;

		std::vector<tst_t> tst_vec = find_state_ids(result_vec);

		for (size_t i = 0; i < tst_vec.size(); ++i) {
			tst_t tst = tst_vec[i];
			int state_id = tst.state_id;
			if (state_id > -1)
			{
				kalman_vec[state_id].set_delta_time(dT);
				kalman_vec[state_id].correct(result_vec_pred[state_id]);
			}
		}

		result_vec = predict();

		global_last_time = std::chrono::steady_clock::now();

		return result_vec;
	}

};
